class_docstr: tests for BinaryCrossesntropy StrongConvex loss.
docstr: Test initialization for given arguments.
  
  Args:
    reg_lambda: initialization value for reg_lambda arg
    c: initialization value for C arg
    radius_constant: initialization value for radius_constant arg
    delta: the delta parameter for the huber loss
docstr_tok: ["Test","initialization","for","given","arguments",".","Args",":","reg_lambda",":","initialization","value","for","reg_lambda","arg","c",":","initialization","value","for","C","arg","radius_constant",":","initialization","value","for","radius_constant","arg","delta",":","the","delta","parameter","for","the","huber","loss"]
code: def test_init_params(self, reg_lambda, c, radius_constant, delta):
      """Test initialization for given arguments.
  
      Args:
        reg_lambda: initialization value for reg_lambda arg
        c: initialization value for C arg
        radius_constant: initialization value for radius_constant arg
        delta: the delta parameter for the huber loss
      """
      # test valid domains for each variable
      loss = StrongConvexHuber(reg_lambda, c, radius_constant, delta)
      self.assertIsInstance(loss, StrongConvexHuber)
  
imports: from contextlib import contextmanager
  from io import StringIO
  import sys
  from absl.testing import parameterized
  import tensorflow.compat.v1
  from tensorflow.compat.v1.python.framework import test_util
  from tensorflow.compat.v1.python.keras import keras_parameterized
  from tensorflow.compat.v1.python.keras.regularizers import L1L2
  from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexBinaryCrossentropy
  from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexHuber
  from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexMixin
label_desc: Use image node_id 1 for calling the HuberTests obj's underlying member method code with example usage: obj.test_init_params(reg_lambda, c, radius_constant, delta) without return types